New submission from Germán Méndez Bravo :
As the size of a Python project increases, the number of modules and the
complexity of its dependencies increases too, producing two problems in large
codebases: increased risk of import cycles and slow start times due to the
number of modules
Germán Méndez Bravo added the comment:
I added a pull request with my fix here:
https://github.com/python/cpython/pull/27017
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Python tracker
<https://bugs.python.org/issue41
Change by Germán Méndez Bravo :
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keywords: +patch
pull_requests: +25576
stage: -> patch review
pull_request: https://github.com/python/cpython/pull/27017
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Python tracker
<https://bugs.python.org/issu
Germán Méndez Bravo added the comment:
Nils, unfortunately, fixing the MRO here won’t fix the issue because
`TypedDict.__annotations__` in the class copies the annotations from the parent
classes, and when the type evaluation is made, it’s made using the copied
annotation found
Germán Méndez Bravo added the comment:
The way I fixed this is I added `__forward_module__` to `typing.ForwardRef`, so
that it can resolve the forward reference with the same globals as the ones
specified by the module in `__forward_module__`. `TypedDict`'s metaclass should
then pass
Change by Germán Méndez Bravo :
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keywords: +patch
pull_requests: +23506
stage: -> patch review
pull_request: https://github.com/python/cpython/pull/24735
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Python tracker
<https://bugs.python.org/issu
New submission from Germán Méndez Bravo :
A call to `importlib.__import__()` normally locks the import for the module
being worked on; this, however has a performance impact for modules that are
already imported and fully initialized. An example of this are inline
`__import__()` calls
